{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 6. Bayes Classification </h2>\n",
    "\n",
    "This notebook has the code for the charts in Chapter 6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT=!gcloud config get-value project\n",
    "PROJECT=PROJECT[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "BUCKET = PROJECT + '-dsongcp' # REPLACE WITH YOUR BUCKET NAME if this won't work\n",
    "REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1\n",
    "\n",
    "os.environ['BUCKET'] = BUCKET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Exploration using BigQuery </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import google.cloud.bigquery as bigquery\n",
    "\n",
    "bq = bigquery.Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sql = \"\"\"\n",
    "SELECT DISTANCE, DEP_DELAY\n",
    "FROM dsongcp.flights_tzcorr\n",
    "WHERE RAND() < 0.001 AND dep_delay > -20 AND dep_delay < 30 AND distance < 2000\n",
    "\"\"\"\n",
    "df = bq.query(sql).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "g = sns.jointplot(x=df['DISTANCE'], y=df['DEP_DELAY'], kind=\"hex\", height=10, joint_kws={'gridsize':20})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Set up views in Spark SQL </h3>\n",
    "\n",
    "Start a Spark Session if necessary and get a handle to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pyspark.sql.session.SparkSession object at 0x7f65a45cf820>\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession \\\n",
    "    .builder \\\n",
    "    .appName(\"Bayes classification using Spark\") \\\n",
    "    .getOrCreate()\n",
    "print(spark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a table definition (this is done lazily; the files won't be read until we issue a query):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "inputs = 'gs://{}/flights/tzcorr/all_flights-00000-*'.format(BUCKET) # 1/30th\n",
    "#inputs = 'gs://{}/flights/tzcorr/all_flights-*'.format(BUCKET)  # FULL\n",
    "flights = spark.read.json(inputs)\n",
    "\n",
    "# this view can now be queried ...\n",
    "flights.createOrReplaceTempView('flights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StructType(List(StructField(ARR_AIRPORT_LAT,DoubleType,true),StructField(ARR_AIRPORT_LON,DoubleType,true),StructField(ARR_AIRPORT_TZOFFSET,DoubleType,true),StructField(ARR_DELAY,DoubleType,true),StructField(ARR_TIME,StringType,true),StructField(CANCELLED,BooleanType,true),StructField(CRS_ARR_TIME,StringType,true),StructField(CRS_DEP_TIME,StringType,true),StructField(DEP_AIRPORT_LAT,DoubleType,true),StructField(DEP_AIRPORT_LON,DoubleType,true),StructField(DEP_AIRPORT_TZOFFSET,DoubleType,true),StructField(DEP_DELAY,DoubleType,true),StructField(DEP_TIME,StringType,true),StructField(DEST,StringType,true),StructField(DEST_AIRPORT_SEQ_ID,StringType,true),StructField(DISTANCE,StringType,true),StructField(DIVERTED,BooleanType,true),StructField(FL_DATE,StringType,true),StructField(ORIGIN,StringType,true),StructField(ORIGIN_AIRPORT_SEQ_ID,StringType,true),StructField(TAXI_IN,DoubleType,true),StructField(TAXI_OUT,DoubleType,true),StructField(UNIQUE_CARRIER,StringType,true),StructField(WHEELS_OFF,StringType,true),StructField(WHEELS_ON,StringType,true)))\n"
     ]
    }
   ],
   "source": [
    "# Note that distance is STRING\n",
    "print(flights.schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example query over the view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 1:=======================================================> (33 + 1) / 34]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+\n",
      "|count(1)|\n",
      "+--------+\n",
      "| 5357273|\n",
      "+--------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "results = spark.sql('SELECT COUNT(*) FROM flights WHERE dep_delay > -20 AND CAST(distance AS FLOAT) < 2000')\n",
    "results.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Restrict to train days </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a CSV file of the training days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "sql = \"\"\"\n",
    "SELECT *\n",
    "FROM dsongcp.trainday\n",
    "\"\"\"\n",
    "df = bq.query(sql).to_dataframe()\n",
    "df.to_csv('trainday.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FL_DATE,is_train_day\n",
      "2015-01-01,True\n",
      "2015-01-02,False\n"
     ]
    }
   ],
   "source": [
    "!head -3 trainday.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Copying file://trainday.csv [Content-Type=text/csv]...\n",
      "/ [1 files][  5.8 KiB/  5.8 KiB]                                                \n",
      "Operation completed over 1 objects/5.8 KiB.                                      \n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "gsutil cp trainday.csv gs://${BUCKET}/flights/trainday.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create dataframe of traindays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.types import StructType, StructField, StringType, BooleanType\n",
    "schema = StructType([\n",
    "    StructField('FL_DATE', StringType(), True),\n",
    "    StructField('is_train_day', BooleanType(), True)\n",
    "])\n",
    "traindays = spark.read \\\n",
    "    .option(\"header\", \"true\") \\\n",
    "    .option(\"inferSchema\", \"true\") \\\n",
    "    .csv('gs://{}/flights/trainday.csv'.format(BUCKET))\n",
    "\n",
    "traindays.createOrReplaceTempView('traindays')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(FL_DATE='2015-01-01', is_train_day=True),\n",
       " Row(FL_DATE='2015-01-02', is_train_day=False),\n",
       " Row(FL_DATE='2015-01-03', is_train_day=False),\n",
       " Row(FL_DATE='2015-01-04', is_train_day=True),\n",
       " Row(FL_DATE='2015-01-05', is_train_day=True)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = spark.sql('SELECT * FROM traindays')\n",
    "results.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "statement = \"\"\"\n",
    "SELECT\n",
    "  f.FL_DATE AS date,\n",
    "  CAST(distance AS FLOAT) AS distance,\n",
    "  dep_delay,\n",
    "  IF(arr_delay < 15, 1, 0) AS ontime\n",
    "FROM flights f\n",
    "JOIN traindays t\n",
    "ON f.FL_DATE == t.FL_DATE\n",
    "WHERE\n",
    "  t.is_train_day AND\n",
    "  f.dep_delay IS NOT NULL\n",
    "ORDER BY\n",
    "  f.dep_delay DESC\n",
    "\"\"\"\n",
    "flights = spark.sql(statement)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Hexbin plot </h3>\n",
    "\n",
    "Create a hexbin plot using Spark (repeat of what we did in BigQuery, except that we are now restricting to train days only)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 11:=====================================================>  (23 + 1) / 24]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------+------------------+--------------------+------------------+\n",
      "|summary|      date|          distance|           dep_delay|            ontime|\n",
      "+-------+----------+------------------+--------------------+------------------+\n",
      "|  count|   3408823|           3408823|             3408823|           3408823|\n",
      "|   mean|      null| 710.3082888140569|-0.17926451446731026|0.9110751716941595|\n",
      "| stddev|      null|442.33750484293574|   8.238764129540925|0.2846352525312672|\n",
      "|    min|2015-01-01|              31.0|               -19.0|                 0|\n",
      "|    max|2015-12-30|            1999.0|                29.0|                 1|\n",
      "+-------+----------+------------------+--------------------+------------------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "df = flights[(flights['distance'] < 2000) & (flights['dep_delay'] > -20) & (flights['dep_delay'] < 30)]\n",
    "df.describe().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Row(date='2015-02-24', distance=404.0, dep_delay=29.0, ontime=1),\n",
       " Row(date='2015-07-18', distance=404.0, dep_delay=29.0, ontime=0),\n",
       " Row(date='2015-03-07', distance=404.0, dep_delay=29.0, ontime=0),\n",
       " Row(date='2015-04-19', distance=404.0, dep_delay=29.0, ontime=0),\n",
       " Row(date='2015-08-06', distance=404.0, dep_delay=29.0, ontime=0)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample the dataframe so that it fits into memory (not a problem in development, but will be on full dataset); then plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf = df.sample(False, 0.02, 20).toPandas()  # to 100,000 rows approx on complete dataset\n",
    "g = sns.jointplot(x=pdf['distance'], y=pdf['dep_delay'], kind=\"hex\", height=10, joint_kws={'gridsize':20})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Quantization </h3>\n",
    "\n",
    "Now find the quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[31.0, 328.0, 541.0, 802.0, inf]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distthresh = flights.approxQuantile('distance', list(np.arange(0, 1.0, 0.2)), 0.02)\n",
    "distthresh[-1] = float('inf')\n",
    "distthresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[-82.0, -6.0, -3.0, 0.0, inf]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delaythresh = flights.approxQuantile('dep_delay', list(np.arange(0, 1.0, 0.2)), 0.05)\n",
    "delaythresh[-1] = float('inf')\n",
    "delaythresh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do Bayes in each bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0.9806362843143835\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 0.9741858639162924\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2 0.9643660979674588\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3 0.5499478274565393\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0 0.9782601010993672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 0.9756200527704485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 0.9681892594613681\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 95:======================================================> (33 + 1) / 34]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 3 0.5760215672613819\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "import pyspark.sql.functions as F\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(columns=['dist_thresh', 'delay_thresh', 'frac_ontime'])\n",
    "for m in range(0, 2): # len(distthresh)-1):\n",
    "    for n in range(0, len(delaythresh)-1):\n",
    "        bdf = flights[(flights['distance'] >= distthresh[m])\n",
    "             & (flights['distance'] < distthresh[m+1])\n",
    "             & (flights['dep_delay'] >= delaythresh[n])\n",
    "             & (flights['dep_delay'] < delaythresh[n+1])]\n",
    "        ontime_frac = bdf.agg(F.sum('ontime')).collect()[0][0] / bdf.agg(F.count('ontime')).collect()[0][0]\n",
    "        print (m, n, ontime_frac)\n",
    "        df = df.append({\n",
    "            'dist_thresh': distthresh[m], \n",
    "            'delay_thresh': delaythresh[n],\n",
    "            'frac_ontime': ontime_frac\n",
    "        }, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get close to the 70% threshold only on the last bin.\n",
    "Let's fine tune the delay thresh around the decision boundary.\n",
    "Which we know is on the order of 15 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0.8137151469480031\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 0.7897361647361647\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2 0.7649991098451131\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3 0.7224041610479677\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 4 0.6874006359300477\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 5 0.6392123650222317\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 6 0.6103401969561325\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 7 0.558261700095511\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 8 0.528104241185488\n"
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     "text": [
      "                                                                                \r"
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      "                                                                                \r"
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      "                                                                                \r"
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      "                                                                                \r"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
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     "name": "stderr",
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      "                                                                                \r"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
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      "1 6 0.6500836120401338\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
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    {
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     "output_type": "stream",
     "text": [
      "                                                                                \r"
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     "output_type": "stream",
     "text": [
      "                                                                                \r"
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     "text": [
      "                                                                                \r"
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      "                                                                                \r"
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      "                                                                                \r"
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     "text": [
      "                                                                                \r"
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     "text": [
      "                                                                                \r"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
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     "output_type": "stream",
     "text": [
      "                                                                                \r"
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     "text": [
      "                                                                                \r"
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     "output_type": "stream",
     "text": [
      "                                                                                \r"
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     "output_type": "stream",
     "text": [
      "                                                                                \r"
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     "output_type": "stream",
     "text": [
      "                                                                                \r"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
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      "3 4 0.7630776099412159\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
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     "text": [
      "3 5 0.7371929008757653\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
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    {
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      "3 6 0.7197072838665759\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
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     "output_type": "stream",
     "text": [
      "3 7 0.6874393150322182\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 383:===================================================>   (32 + 2) / 34]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 8 0.6658182503770739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "delaythresh = range(10, 20)\n",
    "\n",
    "df = pd.DataFrame(columns=['dist_thresh', 'delay_thresh', 'frac_ontime'])\n",
    "for m in range(0, len(distthresh)-1):\n",
    "    for n in range(0, len(delaythresh)-1):\n",
    "        bdf = flights[(flights['distance'] >= distthresh[m])\n",
    "             & (flights['distance'] < distthresh[m+1])\n",
    "             & (flights['dep_delay'] >= delaythresh[n])\n",
    "             & (flights['dep_delay'] < delaythresh[n+1])]\n",
    "        ontime_frac = bdf.agg(F.sum('ontime')).collect()[0][0] / bdf.agg(F.count('ontime')).collect()[0][0]\n",
    "        print (m, n, ontime_frac)\n",
    "        df = df.append({\n",
    "            'dist_thresh': distthresh[m], \n",
    "            'delay_thresh': delaythresh[n],\n",
    "            'frac_ontime': ontime_frac\n",
    "        }, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dist_thresh</th>\n",
       "      <th>delay_thresh</th>\n",
       "      <th>frac_ontime</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.687401</td>\n",
       "      <td>0.012599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>328.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.694893</td>\n",
       "      <td>0.005107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>541.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.706174</td>\n",
       "      <td>0.006174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>802.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.687439</td>\n",
       "      <td>0.012561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dist_thresh  delay_thresh  frac_ontime     score\n",
       "4          31.0          14.0     0.687401  0.012599\n",
       "14        328.0          15.0     0.694893  0.005107\n",
       "23        541.0          15.0     0.706174  0.006174\n",
       "34        802.0          17.0     0.687439  0.012561"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['score'] = abs(df['frac_ontime'] - 0.7)\n",
    "bayes = df.sort_values(['score']).groupby('dist_thresh').head(1).sort_values('dist_thresh')\n",
    "bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dist_thresh,delay_thresh,frac_ontime,score\n",
      "31.0,14.0,0.6874006359300477,0.012599364069952212\n",
      "328.0,15.0,0.6948927283083349,0.005107271691665027\n",
      "541.0,15.0,0.7061742006615215,0.006174200661521523\n",
      "802.0,17.0,0.6874393150322182,0.012560684967781732\n"
     ]
    }
   ],
   "source": [
    "bayes.to_csv('gs://${BUCKET}/flights/bayes.csv'.format(BUCKET), index=False)\n",
    "!gsutil cat gs://{BUCKET}/flights/bayes.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes[ bayes['dist_thresh'] == distthresh[1] ]['delay_thresh'].values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate model\n",
    "\n",
    "Use the decision threshold to see how well model does on held-out flights in the first bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT\n",
      "  '328-541 miles' AS bin,\n",
      "  ROUND(SUM(IF(dep_delay < 15.000000 AND arr_delay < 15, 1, 0))/COUNT(*), 2) AS correct_nocancel,\n",
      "  ROUND(SUM(IF(dep_delay >= 15.000000 AND arr_delay < 15, 1, 0))/COUNT(*), 2) AS false_positive,\n",
      "  ROUND(SUM(IF(dep_delay < 15.000000 AND arr_delay >= 15, 1, 0))/COUNT(*), 2) AS false_negative,\n",
      "  ROUND(SUM(IF(dep_delay >= 15.000000 AND arr_delay >= 15, 1, 0))/COUNT(*), 2) AS correct_cancel,\n",
      "  COUNT(*) AS total_flights\n",
      "FROM flights f\n",
      "JOIN traindays t\n",
      "ON f.FL_DATE == t.FL_DATE\n",
      "WHERE\n",
      "  t.is_train_day == 'False' AND\n",
      "  f.distance >= 328.000000 AND f.distance < 541.000000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 387:=====================================================> (33 + 1) / 34]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|          bin|correct_nocancel|false_positive|false_negative|correct_cancel|total_flights|\n",
      "+-------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|328-541 miles|            0.79|          0.03|          0.03|          0.14|       316767|\n",
      "+-------------+----------------+--------------+--------------+--------------+-------------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "m = 1\n",
    "statement = \"\"\"\n",
    "SELECT\n",
    "  '{0:.0f}-{1:.0f} miles' AS bin,\n",
    "  ROUND(SUM(IF(dep_delay < {2:f} AND arr_delay < 15, 1, 0))/COUNT(*), 2) AS correct_nocancel,\n",
    "  ROUND(SUM(IF(dep_delay >= {2:f} AND arr_delay < 15, 1, 0))/COUNT(*), 2) AS false_positive,\n",
    "  ROUND(SUM(IF(dep_delay < {2:f} AND arr_delay >= 15, 1, 0))/COUNT(*), 2) AS false_negative,\n",
    "  ROUND(SUM(IF(dep_delay >= {2:f} AND arr_delay >= 15, 1, 0))/COUNT(*), 2) AS correct_cancel,\n",
    "  COUNT(*) AS total_flights\n",
    "FROM flights f\n",
    "JOIN traindays t\n",
    "ON f.FL_DATE == t.FL_DATE\n",
    "WHERE\n",
    "  t.is_train_day == 'False' AND\n",
    "  f.distance >= {0:f} AND f.distance < {1:f}\n",
    "\"\"\".format( distthresh[m], distthresh[m+1], bayes[ bayes['dist_thresh'] == distthresh[m] ]['delay_thresh'].values[0] )\n",
    "print(statement)\n",
    "eval_flights = spark.sql(statement)\n",
    "eval_flights.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|         bin|correct_nocancel|false_positive|false_negative|correct_cancel|total_flights|\n",
      "+------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|31-328 miles|            0.79|          0.03|          0.03|          0.13|       321393|\n",
      "+------------+----------------+--------------+--------------+--------------+-------------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|          bin|correct_nocancel|false_positive|false_negative|correct_cancel|total_flights|\n",
      "+-------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|328-541 miles|            0.79|          0.03|          0.03|          0.14|       316767|\n",
      "+-------------+----------------+--------------+--------------+--------------+-------------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|          bin|correct_nocancel|false_positive|false_negative|correct_cancel|total_flights|\n",
      "+-------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|541-802 miles|            0.78|          0.04|          0.04|          0.14|       316613|\n",
      "+-------------+----------------+--------------+--------------+--------------+-------------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 419:=====================================================> (33 + 1) / 34]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|             bin|correct_nocancel|false_positive|false_negative|correct_cancel|total_flights|\n",
      "+----------------+----------------+--------------+--------------+--------------+-------------+\n",
      "|802-100000 miles|            0.77|          0.04|          0.05|          0.13|       665693|\n",
      "+----------------+----------------+--------------+--------------+--------------+-------------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "distthresh[-1] = 100000 # bug in Spark SQL, which treats 'inf' as a column name\n",
    "for m in range(0, len(distthresh)-1):\n",
    "    statement = \"\"\"\n",
    "SELECT\n",
    "  '{0:.0f}-{1:.0f} miles' AS bin,\n",
    "  ROUND(SUM(IF(dep_delay < {2:f} AND arr_delay < 15, 1, 0))/COUNT(*), 2) AS correct_nocancel,\n",
    "  ROUND(SUM(IF(dep_delay >= {2:f} AND arr_delay < 15, 1, 0))/COUNT(*), 2) AS false_positive,\n",
    "  ROUND(SUM(IF(dep_delay < {2:f} AND arr_delay >= 15, 1, 0))/COUNT(*), 2) AS false_negative,\n",
    "  ROUND(SUM(IF(dep_delay >= {2:f} AND arr_delay >= 15, 1, 0))/COUNT(*), 2) AS correct_cancel,\n",
    "  COUNT(*) AS total_flights\n",
    "FROM flights f\n",
    "JOIN traindays t\n",
    "ON f.FL_DATE == t.FL_DATE\n",
    "WHERE\n",
    "  t.is_train_day == 'False' AND\n",
    "  f.distance >= {0:f} AND f.distance < {1:f}\n",
    "\"\"\".format( distthresh[m], distthresh[m+1], bayes[ bayes['dist_thresh'] == distthresh[m] ]['delay_thresh'].values[0] )\n",
    "    eval_flights = spark.sql(statement)\n",
    "    eval_flights.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Repeat, but on full dataset </h2>\n",
    "\n",
    "You can launch the above processing on the full dataset from within JupyterLab if you want the statistics and graphs updated. I didn't, though, because this is not what I would have really done. Instead, \n",
    "I would have created a standalone Python script and submitted it to the cluster -- there is no need to put JupyterLab in the middle of a production process. We'll submit a standalone Pig program to the cluster in the next section.\n",
    "\n",
    "Steps:\n",
    "<ol>\n",
    "<li> Change the input variable to process all-flights-* </li>\n",
    "<li> Increase cluster size (bash increase_cluster.sh from CloudShell) </li>\n",
    "<li> Clear all Outputs from this notebook </li>\n",
    "<li> Restart Kernel and Run all cells </li>\n",
    "<li> Decrease cluster size (bash decrease_cluster.sh from CloudShell) </li>\n",
    "</ol>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2019 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
   ]
  }
 ],
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